Environmental sensors are devices that are capable of detecting the status of the ambient environmental conditions in which the sensor is placed and can be used to detect changes in the environment. The particular environmental conditions that are detected can vary widely. That is, environmental sensors can sense environmental conditions such as: (1) sound, (2) light, (3) pressure (both air pressure and the amount of pressure that is directly exerted on the sensor by a solid in contact with the sensor), (4) touch, (5) motion, (6) temperature, (7) moisture, (8) humidity, (9) air flow; (10) fluid flow, etc. The use of such environmental sensors as IoT (Internet of things) devices is becoming ubiquitous in recent times, with the rise of environmental monitoring, and automated monitor and control systems for controlling, monitoring and affecting changes in the systems, machines and devices in which such sensors might be found. “IoT” or “Internet of things” is an informal term that is commonly used to refer to nonstandard computing devices that connect wirelessly to a network and have the ability to transmit data. The term relates to extending Internet connectivity beyond standard devices, such as desktops, laptops, smartphones and tablets, to a range of traditionally simple devices or physical devices that are not typically capable of communicating over the Internet, including everyday objects, such as audio speakers, toys, refrigerators, cars, watches, car keys, etc. However, when embedded with technology, these devices can communicate and interact over the Internet. Providing the ability to communicate over the Internet allows them to be remotely monitored and controlled and to receive information from remote devices and databases. In addition, several smart sensors are included within the scope of IoT devices. Such sensors can be used in systems such as smart meters, water meters, electric meters, etc., commercial security systems, smart city technology systems, etc. The number of types of such IoT devices continues to grow as people continue to expand their appreciation for the power of having a communication port to and from such common items.
Some examples in the area of environmental monitoring include cameras used for monitoring activity around a point of interest. Such cameras are found in a variety of locations to monitor the visual environment around such a point of interest and capture images regarding the activities that occur around such points of interest. In some cases, the cameras lie dormant until a particular change in an environmental condition occurs that indicates that an event has occurred that might be worth capturing on the camera. Examples of such a change in the environmental conditions might be: (1) a change in the volume of the ambient sound around the camera (either louder or softer); (2) motion that was not present previously; or (3) change in the amount of ambient light (either more or less ambient light). Several other factors could also be monitored to determine when to turn on the camera.
Supplementing the use of sensors for capturing such information regarding environmental conditions, the recent rise in the development of artificial intelligence and the use of neural networks to analyze information to draw conclusions about particular sets of environmental data have resulted in the use of sensors to collect data that can be analyzed to draw conclusions about the environment and to control systems that interact with the environment. For example, it is common today for cellular telephones to have microphones that can detect acoustic signals (i.e., sound waves), such as the sound of a user's voice. Artificial intelligence systems are used to perform voice recognition. Accordingly, a voice recognition processor can be coupled to the microphone in the cellular phone and used to detect voice commands issued by the user.
Currently, it is not uncommon for a processor that is in relatively close proximity to the sensor to perform at least a first level determination of the nature of the environmental data. That is, in the example of the cellular telephone with voice recognition noted above, a processor within the phone (i.e., in relatively close proximity to the microphone) may perform a first level voice recognition using a processor that fits within the power, size and weight constraints of the phone. Since the phone is typically capable of communicating with more powerful processor over the Internet, the output from the first level voice recognition performed locally in the phone is provided to the more powerful external processors to do the “heavy lifting” necessary to determine the nature of the request and to provide a response. In such cases, processing is performed in the phone by a programmable processor. As such, the processor is relatively complex, consumes a relatively large amount of power and has a relatively large size. In addition, the phone still relies on an external processing capability to perform complex pattern recognition that is typically performed by a neural network.
As artificial intelligence continues to proliferate and the recognition of more applications increases, the need for inexpensive, lightweight, small sensors will continue to grow. In addition, the number of different applications for such sensors will grow as well. Furthermore, the growth in the use of IoT devices that provide information about the environment in which they exist will result in a need for more bandwidth to communicate the information gathered and shared by such IoT devices. Such devices include smartphones, wearables, smart speakers, surveillance cameras, drones, machine vision robots, etc. These devices may be called upon to perform facial recognition, speech recognition, license plate recognition, fault detection, collision avoidance, etc. In addition, to bandwidth considerations, communications over the Internet make the data that is collected susceptible to interference. It can be seen that a larger amount of data being communicated means a greater likelihood of an error in the communication of that data, and a greater energy usage for transmitting raw data thereby constraining the use of solution in a wide variety of battery powered edge node scenarios. Furthermore, data that is transmitted to gateways within the Internet must be stored, taking up resources. Therefore, the greater the number of such devices, the larger the resources that need be available generally. Still further, people are becoming more aware of the need to be efficient in the use of power. Increasing the number of devices that require power to communicate data increases the amount of overall power required generally, in addition to the increase in the network or Internet traffic. In addition to these concerns, the latency between the processes that occur at the IoT device and the processes that are performed by other devices to which the IoT device interfaces through the Internet can cause IoT systems to be less efficient or less effective.
Another consideration in determine the architecture of sensors for use in controlling and monitoring systems is the manner in which the information from the sensors is processed to make decisions regarding the manner in which the system will react to various changes in the environment and detected events. Modern computers and edge processing devices are limited in many cases from achieving further performance gains by what is commonly known as the “memory wall problem”. The memory wall problem is defined as the situation that occurs when a much faster processor is used with a relatively slow dynamic random access memory (DRAM). The mismatch in the speed results in the processor speed being masked by the relatively slow DRAM speed. Therefore, if the speed of memory available today does not keep up with the speed of the processors that rely upon that memory, the advantages of the faster processors will not be felt.
Therefore, there is currently a need and there will be an even greater need in the future, for sensors that can efficiently provide information to monitor and control systems that use environmental information to monitor, control and effect changes in systems in which the sensor is a component (i.e., sensors that use less power, bandwidth and size). In addition, there is a need in some cases for IoT and IIoT (industrial Internet of things) systems that can make decisions more quickly (i.e., with less latency). Furthermore, there is a need for systems that require less overall storage resources.